Contemporary Customer Relationship Management (CRM) systems frequently suffer from fragmented data silos, inconsistent lead tracking mechanisms, and limited analytical capabilities, which collectively hinder informed decision-making in dynamic sales environments. This paper presents an intelligent CRM framework that integrates real-time customer analytics, predictive lead scoring, and automated decision support within a unified architecture. The proposed system consolidates heterogeneous customer interaction data—sourced from transactional logs, web activity streams, and communication records—into a structured analytical pipeline. At the core of the framework lies a predictive modeling layer that estimates the likelihood of lead conversion using a probabilistic formulation, expressed as $P(y=1 \mid \mathbf{x}) = \frac{1}{1 + e^{-(\boldsymbol{\beta}^\top \mathbf{x})}}$, where $\mathbf{x}$ represents behavioral and demographic features, and $\boldsymbol{\beta}$ denotes learned model parameters. Feature importance is dynamically adjusted through a weighted scoring function, $S(L) = \sum_{i=1}^{n} w_i x_i$, enabling adaptive prioritization of leads based on evolving customer engagement patterns. The system further incorporates a streaming analytics module that employs sliding window aggregation to capture temporal variations in user activity, facilitating near real-time insight generation. Experimental evaluation is conducted on a benchmark CRM dataset augmented with synthetic interaction records to simulate realistic sales pipelines. Results indicate a measurable improvement in lead conversion prediction accuracy, achieving an F1-score exceeding conventional rule-based systems, alongside a significant reduction in manual intervention for follow-up management. The integration of automated decision rules with predictive outputs enhances operational efficiency while maintaining interpretability. The primary contribution of this work lies in the design and validation of a scalable, AI-driven CRM framework that unifies data integration, predictive analytics, and decision automation to support intelligent customer lifecycle management.

Keywords: CRM, Predictive Analytics, Lead Scoring, Machine Learning, Sales Automation, Customer Intelligence, Decision Support Systems